Zum Hauptinhalt springen

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall.

Bargiotas, I ; Wang, D ; et al.
In: Journal of neurology, Jg. 270 (2023-02-01), Heft 2, S. 618-631
Online academicJournal

Titel:
Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall.
Autor/in / Beteiligte Person: Bargiotas, I ; Wang, D ; Mantilla, J ; Quijoux, F ; Moreau, A ; Vidal, C ; Barrois, R ; Nicolai, A ; Audiffren, J ; Labourdette, C ; Bertin-Hugaul, F ; Oudre, L ; Buffat, S ; Yelnik, A ; Ricard, D ; Vayatis, N ; Vidal, PP
Link:
Zeitschrift: Journal of neurology, Jg. 270 (2023-02-01), Heft 2, S. 618-631
Veröffentlichung: Berlin ; New York, Springer-Verlag, 2023
Medientyp: academicJournal
ISSN: 1432-1459 (electronic)
DOI: 10.1007/s00415-022-11251-3
Schlagwort:
  • Humans
  • Aged
  • Fear
  • Machine Learning
  • Quality of Life
  • Frailty
Sonstiges:
  • Nachgewiesen in: MEDLINE
  • Sprachen: English
  • Publication Type: Journal Article; Review
  • Language: English
  • [J Neurol] 2023 Feb; Vol. 270 (2), pp. 618-631. <i>Date of Electronic Publication: </i>2022 Jul 11.
  • MeSH Terms: Quality of Life* ; Frailty* ; Humans ; Aged ; Fear ; Machine Learning
  • References: Allen NE, Schwarzel AK, Canning CG (2013) Recurrent falls in Parkinson’s disease: a systematic review. Parkinsons Dis 2013:1–16. https://doi.org/10.1155/2013/906274. (PMID: 10.1155/2013/906274) ; Fernando E, Fraser M, Hendriksen J et al (2017) Risk factors associated with falls in older adults with dementia: a systematic review. Physiother Canada 69:161–170. https://doi.org/10.3138/ptc.2016-14. (PMID: 10.3138/ptc.2016-14) ; Tinetti ME (2003) Clinical practice. Preventing falls in elderly persons. N Engl J Med 348:42–49. https://doi.org/10.1056/NEJMcp020719. (PMID: 10.1056/NEJMcp020719) ; Melzer I, Benjuya N, Kaplanski J (2004) Postural stability in the elderly: a comparison between fallers and non-fallers. Age Ageing 33:602–607. (PMID: 10.1093/ageing/afh218) ; Bergen G, Stevens MR, Burns ER (2016) Falls and fall injuries among adults aged ≥65 years—United States, 2014. MMWR Morb Mortal Wkly Rep 65:993–998. https://doi.org/10.15585/mmwr.mm6537a2. (PMID: 10.15585/mmwr.mm6537a2) ; Scheffer AC, Schuurmans MJ, van Dijk N et al (2008) Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing 37:19–24. https://doi.org/10.1093/ageing/afm169. (PMID: 10.1093/ageing/afm169) ; Sylliaas H, Selbæk G, Bergland A (2012) Do behavioral disturbances predict falls among nursing home residents? Aging Clin Exp Res 24:251–256. https://doi.org/10.1007/BF03325253. (PMID: 10.1007/BF03325253) ; Schniepp R, Huppert A, Decker J et al (2021) Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring. J Neurol 268:3421–3434. (PMID: 10.1007/s00415-021-10504-x) ; Jahn K, Zwergal A, Schniepp R (2010) Gait disturbances in old age: classification, diagnosis, and treatment from a neurological perspective. Dtsch Arztebl Int 107:306. ; Maki BE, Zecevic A, Bateni H et al (2001) Cognitive demands of executing postural reactions: does aging impede attention switching? NeuroReport 12:3583–3587. https://doi.org/10.1097/00001756-200111160-00042. (PMID: 10.1097/00001756-200111160-00042) ; Zwergal A, Linn J, Xiong G et al (2012) Aging of human supraspinal locomotor and postural control in fMRI. Neurobiol Aging 33:1073–1084. (PMID: 10.1016/j.neurobiolaging.2010.09.022) ; Menant JC, Schoene D, Sarofim M, Lord SR (2014) Single and dual task tests of gait speed are equivalent in the prediction of falls in older people: a systematic review and meta-analysis. Ageing Res Rev 16:83–104. https://doi.org/10.1016/j.arr.2014.06.001. (PMID: 10.1016/j.arr.2014.06.001) ; Kearney FC, Harwood RH, Gladman JRF et al (2013) The relationship between executive function and falls and gait abnormalities in older adults: a systematic review. Dement Geriatr Cogn Disord 36:20–35. https://doi.org/10.1159/000350031. (PMID: 10.1159/000350031) ; Pettersson AF, Olsson E, Wahlund L-O (2005) Motor function in subjects with mild cognitive impairment and early Alzheimer’s disease. Dement Geriatr Cogn Disord 19:299–304. https://doi.org/10.1159/000084555. (PMID: 10.1159/000084555) ; Dieterich M, Brandt T (2019) Perception of verticality and vestibular disorders of balance and falls. Front Neurol 10:172. (PMID: 10.3389/fneur.2019.00172) ; Ganz DA, Bao Y, Shekelle PG, Rubenstein LZ (2007) Will my patient fall? JAMA 297:77–86. (PMID: 10.1001/jama.297.1.77) ; Studenski S, Perera S, Patel K et al (2011) Gait speed and survival in older adults. JAMA 305:50–58. (PMID: 10.1001/jama.2010.1923) ; El-Khoury F, Cassou B, Charles M-A, Dargent-Molina P (2013) The effect of fall prevention exercise programmes on fall induced injuries in community dwelling older adults: systematic review and meta-analysis of randomised controlled trials. BMJ 347. ; da Costa BR, Rutjes AWS, Mendy A et al (2012) Can falls risk prediction tools correctly identify fall-prone elderly rehabilitation inpatients? A systematic review and meta-analysis. PLoS ONE 7:e41061. https://doi.org/10.1371/journal.pone.0041061. (PMID: 10.1371/journal.pone.0041061) ; Vassallo M, Poynter L, Sharma JC et al (2008) Fall risk-assessment tools compared with clinical judgment: an evaluation in a rehabilitation ward. Age Ageing 37:277–281. https://doi.org/10.1093/ageing/afn062. (PMID: 10.1093/ageing/afn062) ; Omaña H, Bezaire K, Brady K et al (2021) Functional reach test, single-leg stance test, and tinetti performance-oriented mobility assessment for the prediction of falls in older adults: a systematic review. Phys Ther 101:pzab173. (PMID: 10.1093/ptj/pzab173) ; Lusardi MM, Fritz S, Middleton A et al (2017) Determining risk of falls in community dwelling older adults: a systematic review and meta-analysis using posttest probability. J Geriatr Phys Ther 40:1. (PMID: 10.1519/JPT.0000000000000099) ; Barry E, Galvin R, Keogh C et al (2014) Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta- analysis. BMC Geriatr 14:14. https://doi.org/10.1186/1471-2318-14-14. (PMID: 10.1186/1471-2318-14-14) ; Quijoux F, Vienne-Jumeau A, Bertin-Hugault F et al (2020) Center of pressure displacement characteristics differentiate fall risk in older people: a systematic review with meta-analysis. Ageing Res Rev 62:101117. (PMID: 10.1016/j.arr.2020.101117) ; Quijoux F, Nicolaï A, Chairi I et al (2021) A review of center of pressure (COP) variables to quantify standing balance in elderly people: algorithms and open-access code. Physiol Rep 9:e15067. (PMID: 10.14814/phy2.15067) ; Cortés OL, Piñeros H, Aya PA et al (2021) Systematic review and meta-analysis of clinical trials: In–hospital use of sensors for prevention of falls. Medicine (Baltimore) 100:e27467. (PMID: 10.1097/MD.0000000000027467) ; Ferreira RN, Ribeiro NF, Santos CP (2022) Fall risk assessment using wearable sensors: a narrative review. Sensors 22:984. (PMID: 10.3390/s22030984) ; Hemmatpour M, Ferrero R, Montrucchio B, Rebaudengo M (2019) A review on fall prediction and prevention system for personal devices: evaluation and experimental results. Adv Human-computer Interact 2019:1–12. (PMID: 10.1155/2019/9610567) ; Montesinos L, Castaldo R, Pecchia L (2018) Wearable inertial sensors for fall risk assessment and prediction in older adults: a systematic review and meta-analysis. IEEE Trans Neural Syst Rehabil Eng 26:573–582. (PMID: 10.1109/TNSRE.2017.2771383) ; Sun R, Sosnoff JJ (2018) Novel sensing technology in fall risk assessment in older adults: a systematic review. BMC Geriatr 18:1–10. (PMID: 10.1186/s12877-018-0706-6) ; Balasubramanian CK (2015) The Community balance and mobility scale alleviates the ceiling effects observed in the currently used gait and balance assessments for the community-dwelling older adults. J Geriatr Phys Ther 38:78–89. https://doi.org/10.1519/JPT.0000000000000024. (PMID: 10.1519/JPT.0000000000000024) ; Mancini M, Horak FB (2010) The relevance of clinical balance assessment tools to differentiate balance deficits. Eur J Phys Rehabil Med 46:239–248. ; Ruhe A, Fejer R, Walker B (2010) The test–retest reliability of centre of pressure measures in bipedal static task conditions—a systematic review of the literature. Gait Posture 32:436–445. https://doi.org/10.1016/j.gaitpost.2010.09.012. (PMID: 10.1016/j.gaitpost.2010.09.012) ; de Sá FA, Junqueira Ferraz Baracat P (2014) Test–retest reliability for assessment of postural stability using center of pressure spatial patterns of three-dimensional statokinesigrams in young health participants. J Biomech 47:2919–2924. https://doi.org/10.1016/j.jbiomech.2014.07.010. (PMID: 10.1016/j.jbiomech.2014.07.010) ; Duarte M, Freitas S, Zatsiorsky V (2011) Control of equilibrium in humans—Sway over sway. Mot Control Oxford Univ Press, Oxford, pp 219–242. ; Ancona S, Faraci FD, Khatab E et al (2021) Wearables in the home-based assessment of abnormal movements in Parkinson’s disease: a systematic review of the literature. J Neurol. https://doi.org/10.1007/s00415-020-10350-3. (PMID: 10.1007/s00415-020-10350-3) ; Doheny EP, Walsh C, Foran T et al (2013) Falls classification using tri-axial accelerometers during the five-times-sit-to-stand test. Gait Posture 38:1021–1025. https://doi.org/10.1016/j.gaitpost.2013.05.013. (PMID: 10.1016/j.gaitpost.2013.05.013) ; Vienne A, Barrois RP, Buffat S et al (2017) Inertial sensors to assess gait quality in patients with neurological disorders: a systematic review of technical and analytical challenges. Front Psychol. https://doi.org/10.3389/fpsyg.2017.00817. (PMID: 10.3389/fpsyg.2017.00817) ; Vienne A, Moreau A, Mantilla J et al (2017) Gaze constraint while walking in progressive multiple sclerosis: a feasibility study. Neurophysiol Clin 47:354. https://doi.org/10.1016/j.neucli.2017.10.046. (PMID: 10.1016/j.neucli.2017.10.046) ; Mantilla J, Wang D, Bargiotas I et al (2020) Motor style at rest and during locomotion in humans. J Neurophysiol 123:2269–2284. https://doi.org/10.1152/jn.00019.2019. (PMID: 10.1152/jn.00019.2019) ; Bargiotas I, Moreau A, Vienne A et al (2018) Balance impairment in radiation induced leukoencephalopathy patients is coupled with altered visual attention in natural tasks. Front Neurol 9:1185. https://doi.org/10.3389/fneur.2018.01185. (PMID: 10.3389/fneur.2018.01185) ; Feise RJ (2002) Do multiple outcome measures require p-value adjustment? BMC Med Res Methodol 2:8. https://doi.org/10.1186/1471-2288-2-8. (PMID: 10.1186/1471-2288-2-8) ; Wood J, Freemantle N, King M, Nazareth I (2014) Trap of trends to statistical significance: likelihood of near significant P value becoming more significant with extra data. BMJ 348:g2215. https://doi.org/10.1136/bmj.g2215. (PMID: 10.1136/bmj.g2215) ; Bourke AK, van de Ven P, Gamble M et al (2010) Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. J Biomech 43:3051–3057. https://doi.org/10.1016/j.jbiomech.2010.07.005. (PMID: 10.1016/j.jbiomech.2010.07.005) ; Massie S, Forbes G, Craw S et al (2018) Fitsense: employing multi-modal sensors in smart homes to predict falls. In: International conference on case-based reasoning. Springer, pp 249–263. ; Kiprijanovska I, Gjoreski H, Gams M (2020) Detection of gait abnormalities for fall risk assessment using wrist-worn inertial sensors and deep learning. Sensors 20:5373. https://doi.org/10.3390/s20185373. (PMID: 10.3390/s20185373) ; Audiffren J, Bargiotas I, Vayatis N et al (2016) A non linear scoring approach for evaluating balance: classification of elderly as fallers and non-fallers. PLoS ONE 11:e0167456. https://doi.org/10.1371/journal.pone.0167456. (PMID: 10.1371/journal.pone.0167456) ; Bargiotas I, Kalogeratos A, Limnios M et al (2021) Revealing posturographic profile of patients with Parkinsonian syndromes through a novel hypothesis testing framework based on machine learning. PLoS ONE 16:e0246790. https://doi.org/10.1371/journal.pone.0246790. (PMID: 10.1371/journal.pone.0246790) ; Bargiotas I, Audiffren J, Vayatis N et al (2018) On the importance of local dynamics in statokinesigram: a multivariate approach for postural control evaluation in elderly. PLoS ONE 13:e0192868. https://doi.org/10.1371/journal.pone.0192868. (PMID: 10.1371/journal.pone.0192868) ; Speiser JL, Callahan KE, Houston DK et al (2021) Machine learning in aging: an example of developing prediction models for serious fall injury in older adults. J Gerontol Ser A 76:647–654. https://doi.org/10.1093/gerona/glaa138. (PMID: 10.1093/gerona/glaa138) ; Tinetti ME (1986) Performance-oriented assessment of mobility problems in elderly patients. J Am Geriatr Soc 34:119–126. https://doi.org/10.1111/j.1532-5415.1986.tb05480.x. (PMID: 10.1111/j.1532-5415.1986.tb05480.x) ; Shumway-Cook A, Brauer S, Woollacott M (2000) Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Phys Ther 80:896–903. (PMID: 10.1093/ptj/80.9.896) ; Perell KL, Nelson A, Goldman RL et al (2001) Fall risk assessment measures: an analytic review. J Gerontol Ser A Biol Sci Med Sci 56:M761–M766. https://doi.org/10.1093/gerona/56.12.M761. (PMID: 10.1093/gerona/56.12.M761) ; Beauchet O, Fantino B, Allali G et al (2011) Timed up and go test and risk of falls in older adults: a systematic review. J Nutr Health Aging 15:933–938. https://doi.org/10.1007/s12603-011-0062-0. (PMID: 10.1007/s12603-011-0062-0) ; Blum L, Korner-Bitensky N (2008) Usefulness of the berg balance scale in stroke rehabilitation: a systematic review. Phys Ther 88:559–566. https://doi.org/10.2522/ptj.20070205. (PMID: 10.2522/ptj.20070205) ; Nicolai A, Audiffren J (2018) Model-space regularization and fully interpretable algorithms for postural control quantification. In: 2018 IEEE 42nd annual computer software and applications conference (COMPSAC). IEEE, pp 177–182. ; Bargiotas I, Moreau A, Vayatis N, Ricard D (2019) Predicting future falls of parkinsonians using posturography and Random Forest. In: 2019 41th annual international conference of the IEEE engineering in medicine and biology society. IEEE, Berlin. ; Bargiotas I, Audiffren J, Vayatis N et al (2019) Local assessment of statokinesigram dynamics in time: an in-depth look at the scoring algorithm. Image Process Line 9:143–157. (PMID: 10.5201/ipol.2019.251) ; Bargiotas I, Kalogeratos A, Limnios M et al (2020) Multivariate two-sample hypothesis testing through AUC maximization for biomedical applications. In: 11th hellenic conference on artificial intelligence, pp 56–59. ; Sun R, Hsieh KL, Sosnoff JJ (2019) Fall risk prediction in multiple sclerosis using postural sway measures: a machine learning approach. Sci Rep 9:16154. https://doi.org/10.1038/s41598-019-52697-2. (PMID: 10.1038/s41598-019-52697-2) ; Clémençon S, Depecker M, Vayatis N (2013) Ranking forests. J Mach Learn Res 14:39–73. ; Eichler N, Raz S, Toledano-Shubi A et al (2022) Automatic and efficient fall risk assessment based on machine learning. Sensors 22:1557. (PMID: 10.3390/s22041557) ; Liu C-L, Lee C-H, Lin P-M (2010) A fall detection system using k-nearest neighbor classifier. Expert Syst Appl 37:7174–7181. (PMID: 10.1016/j.eswa.2010.04.014) ; Breiman L (2001) Random forests. Mach Learn 45:5–32. (PMID: 10.1023/A:1010933404324) ; Chagdes J, Rietdyk S, Haddad J et al (2009) Multiple timescales in postural dynamics associated with vision and a secondary task are revealed by wavelet analysis. Exp Brain Res 197:297. (PMID: 10.1007/s00221-009-1915-1) ; Savadkoohi M, Oladunni T, Thompson LA (2021) Deep neural networks for human’s fall-risk prediction using force-plate time series signal. Expert Syst Appl 182:115220. (PMID: 10.1016/j.eswa.2021.115220) ; Nicolai A, Limnios M, Trouve A, Audiffren J (2021) A langevin-based model with moving posturographic target to quantify postural control. IEEE Trans Neural Syst Rehabil Eng 29:478–487. https://doi.org/10.1109/TNSRE.2021.3057257. (PMID: 10.1109/TNSRE.2021.3057257) ; Podsiadlo D, Richardson S (1991) The Timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 39:142–148. https://doi.org/10.1111/j.1532-5415.1991.tb01616.x. (PMID: 10.1111/j.1532-5415.1991.tb01616.x) ; Pettersson B, Nordin E, Ramnemark A, Lundin-Olsson L (2020) Neither Timed Up and Go test nor Short Physical Performance Battery predict future falls among independent adults aged ≥75 years living in the community. J Frailty Sarcopenia Falls 5:24–30. https://doi.org/10.22540/JFSF-05-024. (PMID: 10.22540/JFSF-05-024) ; de Morton NA, Berlowitz DJ, Keating JL (2008) A systematic review of mobility instruments and their measurement properties for older acute medical patients. Health Qual Life Outcomes 6:44. https://doi.org/10.1186/1477-7525-6-44. (PMID: 10.1186/1477-7525-6-44) ; Vienne-Jumeau A, Quijoux F, Vidal P-P, Ricard D (2020) Wearable inertial sensors provide reliable biomarkers of disease severity in multiple sclerosis: a systematic review and meta-analysis. Ann Phys Rehabil Med 63:138–147. https://doi.org/10.1016/j.rehab.2019.07.004. (PMID: 10.1016/j.rehab.2019.07.004) ; Vienne-Jumeau A, Oudre L, Moreau A et al (2020) Personalized template-based step detection from inertial measurement units signals in multiple sclerosis. Front Neurol. https://doi.org/10.3389/fneur.2020.00261. (PMID: 10.3389/fneur.2020.00261) ; Dadashi F, Mariani B, Rochat S et al (2013) Gait and foot clearance parameters obtained using shoe-worn inertial sensors in a large-population sample of older adults. Sensors 14:443–457. https://doi.org/10.3390/s140100443. (PMID: 10.3390/s140100443) ; Dibble LE, Nicholson DE, Shultz B et al (2004) Sensory cueing effects on maximal speed gait initiation in persons with Parkinson’s disease and healthy elders. Gait Posture 19:215–225. https://doi.org/10.1016/S0966-6362(03)00065-1. (PMID: 10.1016/S0966-6362(03)00065-1) ; Glaister BC, Bernatz GC, Klute GK, Orendurff MS (2007) Video task analysis of turning during activities of daily living. Gait Posture 25:289–294. https://doi.org/10.1016/j.gaitpost.2006.04.003. (PMID: 10.1016/j.gaitpost.2006.04.003) ; Rampp A, Barth J, Schuelein S et al (2015) Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE Trans Biomed Eng 62:1089–1097. https://doi.org/10.1109/TBME.2014.2368211. (PMID: 10.1109/TBME.2014.2368211) ; Dot T, Quijoux F, Oudre L et al (2020) Non-linear template-based approach for the study of locomotion. Sensors 20:1939. https://doi.org/10.3390/s20071939. (PMID: 10.3390/s20071939) ; Mantilla J, Oudre L, Barrois R et al (2017) Template-DTW based on inertial signals: preliminary results for step characterization. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 2267–2270. ; Oudre L, Barrois-Müller R, Moreau T et al (2018) Template-based step detection with inertial measurement units. Sensors 18:4033. https://doi.org/10.3390/s18114033. (PMID: 10.3390/s18114033) ; Vienne-Jumeau A, Oudre L, Moreau A et al (2019) Comparing Gait Trials with Greedy Template Matching. Sensors 19:3089. https://doi.org/10.3390/s19143089. (PMID: 10.3390/s19143089) ; Zhou Y, Zia Ur Rehman R, Hansen C et al (2020) Classification of neurological patients to identify fallers based on spatial-temporal gait characteristics measured by a wearable device. Sensors 20:4098. https://doi.org/10.3390/s20154098. (PMID: 10.3390/s20154098) ; Kumar VC V, Ha S, Sawicki G, Liu CK (2020) Learning a control policy for fall prevention on an assistive walking device. In: 2020 IEEE international conference on robotics and automation (ICRA). IEEE, pp 4833–4840. ; Hsieh C-Y, Shi W-T, Huang H-Y et al (2018) Machine learning-based fall characteristics monitoring system for strategic plan of falls prevention. In: 2018 IEEE international conference on applied system invention (ICASI). IEEE, pp 818–821. ; Noh B, Youm C, Goh E et al (2021) XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes. Sci Rep 11:12183. https://doi.org/10.1038/s41598-021-91797-w. (PMID: 10.1038/s41598-021-91797-w) ; Ye C, Li J, Hao S et al (2020) Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. Int J Med Inform 137:104105. https://doi.org/10.1016/j.ijmedinf.2020.104105. (PMID: 10.1016/j.ijmedinf.2020.104105) ; Nait Aicha A, Englebienne G, Van Schooten KS et al (2018) Deep learning to predict falls in older adults based on daily-life trunk accelerometry. Sensors 18:1654. (PMID: 10.3390/s18051654) ; Tunca C, Salur G, Ersoy C (2020) Deep learning for fall risk assessment with inertial sensors: utilizing domain knowledge in spatio-temporal gait parameters. IEEE J Biomed Heal Informatics 24:1994–2005. https://doi.org/10.1109/JBHI.2019.2958879. (PMID: 10.1109/JBHI.2019.2958879) ; Barrois RP-M, Ricard D, Oudre L et al (2017) Observational study of 180° turning strategies using inertial measurement units and fall risk in poststroke hemiparetic patients. Front Neurol. https://doi.org/10.3389/fneur.2017.00194. (PMID: 10.3389/fneur.2017.00194) ; Bachlin M, Plotnik M, Roggen D et al (2010) Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom. IEEE Trans Inf Technol Biomed 14:436–446. https://doi.org/10.1109/TITB.2009.2036165. (PMID: 10.1109/TITB.2009.2036165) ; Chereshnev R, Kertész-Farkas A (2018) HuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor Networks. pp 131–141. ; Brajdic A, Harle R (2013) Walk detection and step counting on unconstrained smartphones. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing. ACM, New York, NY, USA, pp 225–234. ; Truong C, Barrois-Müller R, Moreau T et al (2019) A data set for the study of human locomotion with inertial measurements units. Image Process Line 9:381–390. https://doi.org/10.5201/ipol.2019.265. (PMID: 10.5201/ipol.2019.265) ; Barrois R, Gregory T, Oudre L et al (2016) An automated recording method in clinical consultation to rate the limp in lower limb osteoarthritis. PLoS ONE 11:e0164975. https://doi.org/10.1371/journal.pone.0164975. (PMID: 10.1371/journal.pone.0164975) ; Ewenczyk C, Mesmoudi S, Gallea C et al (2017) Antisaccades in Parkinson disease: a new marker of postural control? Neurology 88:853–861. (PMID: 10.1212/WNL.0000000000003658) ; Chapman GJ, Hollands MA (2006) Evidence for a link between changes to gaze behaviour and risk of falling in older adults during adaptive locomotion. Gait Posture 24:288–294. (PMID: 10.1016/j.gaitpost.2005.10.002) ; Vitório R, Gobbi LTB, Lirani-Silva E et al (2016) Synchrony of gaze and stepping patterns in people with Parkinson’s disease. Behav Brain Res 307:159–164. (PMID: 10.1016/j.bbr.2016.04.010) ; Ajrezo L, Wiener-Vacher S, Bucci MP (2013) Saccades improve postural control: a developmental study in normal children. PLoS ONE 8:e81066. (PMID: 10.1371/journal.pone.0081066) ; Aguiar SA, Polastri PF, Godoi D et al (2015) Effects of saccadic eye movements on postural control in older adults. Psychol Neurosci 8:19. (PMID: 10.1037/h0100352) ; Leigh RJ, Zee DS (2015) The neurology of eye movements. Oxford University Press, USA. (PMID: 10.1093/med/9780199969289.001.0001) ; Ouchi Y, Okada H, Yoshikawa E et al (1999) Brain activation during maintenance of standing postures in humans. Brain 122:329–338. (PMID: 10.1093/brain/122.2.329) ; Deubel H, Schneider WX (1996) Saccade target selection and object recognition: evidence for a common attentional mechanism. Vision Res 36:1827–1837. (PMID: 10.1016/0042-6989(95)00294-4) ; Rizzolatti G, Riggio L, Dascola I, Umiltá C (1987) Reorienting attention across the horizontal and vertical meridians: evidence in favor of a premotor theory of attention. Neuropsychologia 25:31–40. (PMID: 10.1016/0028-3932(87)90041-8) ; Belenkii VE, Gurfinkel VS, Paltsev EI (1967) On the control elements of voluntary movements. Biofizika. ; Gaymard B, Lynch J, Ploner CJ et al (2003) The parieto-collicular pathway: anatomical location and contribution to saccade generation. Eur J Neurosci 17:1518–1526. (PMID: 10.1046/j.1460-9568.2003.02570.x) ; Bonnet CT, Szaffarczyk S, Baudry S (2017) Functional synergy between postural and visual behaviors when performing a difficult precise visual task in upright stance. Cogn Sci 41:1675–1693. (PMID: 10.1111/cogs.12420) ; Taghvaei S, Jahanandish MH, Kosuge K (2017) Autoregressive-moving-average hidden Markov model for vision-based fall prediction—an application for walker robot. Assist Technol 29:19–27. https://doi.org/10.1080/10400435.2016.1174178. (PMID: 10.1080/10400435.2016.1174178) ; Ting LH, McKay JL (2007) Neuromechanics of muscle synergies for posture and movement. Curr Opin Neurobiol 17:622–628. (PMID: 10.1016/j.conb.2008.01.002) ; Merfeld DM, Zupan L, Peterka RJ (1999) Humans use internal models to estimate gravity and linear acceleration. Nature 398:615. (PMID: 10.1038/19303) ; Bonan IV, Gaillard F, Ponche ST et al (2015) Early post-stroke period: a privileged time for sensory re-weighting? J Rehabil Med 47:516–522. (PMID: 10.2340/16501977-1968) ; Isableu B, Ohlmann T, Crémieux J, Amblard B (2003) Differential approach to strategies of segmental stabilisation in postural control. Exp Brain Res 150:208–221. (PMID: 10.1007/s00221-003-1446-0) ; Lacour M, Barthelemy J, Borel L et al (1997) Sensory strategies in human postural control before and after unilateral vestibular neurotomy. Exp Brain Res 115:300–310. (PMID: 10.1007/PL00005698) ; Vibert N, MacDougall HG, De Waele C et al (2001) Variability in the control of head movements in seated humans: a link with whiplash injuries? J Physiol 532:851–868. (PMID: 10.1111/j.1469-7793.2001.0851e.x) ; Sprager S, Juric MB (2015) Inertial sensor-based gait recognition: a review. Sensors 15:22089–22127. (PMID: 10.3390/s150922089) ; Kikkert LHJ, Vuillerme N, van Campen JP et al (2016) Walking ability to predict future cognitive decline in old adults: a scoping review. Ageing Res Rev 27:1–14. (PMID: 10.1016/j.arr.2016.02.001) ; Mortaza N, Abu Osman NA, Mehdikhani N (2014) Are the spatio-temporal parameters of gait capable of distinguishing a faller from a non-faller elderly. Eur J Phys Rehabil Med 50:677–691. ; Dasenbrock L, Heinks A, Schwenk M, Bauer JM (2016) Technology-based measurements for screening, monitoring and preventing frailty. Z Gerontol Geriatr 49:581–595. (PMID: 10.1007/s00391-016-1129-7) ; Schwenk M, Howe C, Saleh A et al (2014) Frailty and technology: a systematic review of gait analysis in those with frailty. Gerontology 60:79–89. (PMID: 10.1159/000354211) ; Dingwell JB, Cusumano JP (2015) Identifying stride-to-stride control strategies in human treadmill walking. PLoS ONE 10:e0124879. (PMID: 10.1371/journal.pone.0124879) ; Moore IS (2016) Is there an economical running technique? A review of modifiable biomechanical factors affecting running economy. Sport Med 46:793–807. (PMID: 10.1007/s40279-016-0474-4) ; König N, Taylor WR, Baumann CR et al (2016) Revealing the quality of movement: a meta-analysis review to quantify the thresholds to pathological variability during standing and walking. Neurosci Biobehav Rev 68:111–119. (PMID: 10.1016/j.neubiorev.2016.03.035) ; Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30. ; Ribeiro MT, Singh S, Guestrin C (2016) “Why should i trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp 1135–1144. ; Chen C, Li O, Tao C, Barnett AJ, Rudin C, Su JK (2019) This looks like that: deep learning for interpretable image recognition. Adv Neural Informat Process Syst 32. ; Job M, Dottor A, Viceconti A, Testa M (2020) Ecological gait as a fall indicator in older adults: a systematic review. Gerontologist 60:e395–e412. (PMID: 10.1093/geront/gnz113) ; Nouredanesh M, Godfrey A, Howcroft J et al (2021) Fall risk assessment in the wild: a critical examination of wearable sensor use in free-living conditions. Gait Posture 85:178–190. (PMID: 10.1016/j.gaitpost.2020.04.010) ; Rajagopalan R, Litvan I, Jung T-P (2017) Fall prediction and prevention systems: recent trends, challenges, and future research directions. Sensors 17:2509. (PMID: 10.3390/s17112509) ; Zhao G, Chen L, Ning H (2021) Sensor-based fall risk assessment: a survey. In: Healthcare. multidisciplinary digital publishing institute, p 1448. ; Usmani S, Saboor A, Haris M et al (2021) Latest research trends in fall detection and prevention using machine learning: a systematic review. Sensors 21:5134. (PMID: 10.3390/s21155134) ; Tanwar R, Nandal N, Zamani M, Manaf AA (2022) Pathway of trends and technologies in fall detection: a systematic review. In: Healthcare. multidisciplinary digital publishing institute, p 172.
  • Contributed Indexing: Keywords: Fall prediction; Force-platform; Frailty; Longitudinal follow-up; Machine learning; Wearables
  • Entry Date(s): Date Created: 20220711 Date Completed: 20230201 Latest Revision: 20230313
  • Update Code: 20240513
  • PubMed Central ID: PMC9886639

Klicken Sie ein Format an und speichern Sie dann die Daten oder geben Sie eine Empfänger-Adresse ein und lassen Sie sich per Email zusenden.

oder
oder

Wählen Sie das für Sie passende Zitationsformat und kopieren Sie es dann in die Zwischenablage, lassen es sich per Mail zusenden oder speichern es als PDF-Datei.

oder
oder

Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.

xs 0 - 576
sm 576 - 768
md 768 - 992
lg 992 - 1200
xl 1200 - 1366
xxl 1366 -